In the predominant sequence-to-function paradigm, 3-D structure is an obligatory prerequisite for protein function. Even though over 100 counterexamples can be found in literature, generalization of the functions associated with nonfolded (disordered) protein has been mostly ignored. Application of our proprietary bioinformatics software, PONDR, to a comprehensive set of oncogenes revealed that these proteins are likely to contain significantly more disorder than other protein types. This result, combined with indications that disorder is crucial to many protein functions, demand that disorder be considered explicitly in the study of human disease. Thus, cancer-specific data mining will produce "Cancer DisProt," containing correlations of disorder/order with protein function, is proposed. Disorder/order predictions and existing structural knowledge will be correlated with functions of cancer-associated proteins and augmented with local interaction networks to provide an interactive resource tool for cancer-related research. A companion methods manual will facilitate the integration of order/disorder knowledge into novel experiments. Cancer DisProt will be a useful bioinformatics tool for functional annotation of entire genomes. When augmented with methods for studying counter-example proteins, it will provide the basis for design of novel approaches to development of cancer treatments or drug discovery.